This invention relates to a system for measurement of material properties by determination of the response of a sample to incident radiation, and more specifically to the measurement of analytes such as glucose or alcohol in human tissue.
Noninvasive glucose monitoring has been a long-standing objective for many development groups. Several of these groups have sought to use near infrared spectroscopy as the measurement modality. To date, none of these groups has demonstrated a system that generates noninvasive glucose measurements adequate to satisfy both the U.S. Food and Drug Administration (“FDA”) and the physician community. The potential use of near-infrared (near-IR) spectroscopy for noninvasive glucose measurements has attracted significant recent attention. Infrared spectroscopy (IR spectroscopy) is the spectroscopy that deals with the infrared region of the electromagnetic spectrum, that is light with a longer wavelength and lower frequency than visible light. It covers a range of techniques, mostly based on absorption spectroscopy. As with all spectroscopic techniques, it can be used to identify, measure, quantify and study chemicals. The infrared portion of the electromagnetic spectrum is usually divided into three regions; the near-, mid- and far-infrared, named for their relation to the visible spectrum. The higher energy near-IR, approximately 14000-4000 cm-1 (0.8-2.5 μm wavelength) can excite overtone or harmonic vibrations.
Some principles behind a near infrared spectroscopic measurement include (1) to allow near-IR light to penetrate a region of body tissue and thereby excite vibrations in the constituent molecules; (2) to measure the amount of light absorbed as a function of wavelength; and (3) to use the resulting data to construct a calibration model that relates the spectral information to the concentration of blood glucose. The generation of this calibration model requires the measurement of reference glucose concentration values during the spectral data acquisition, typically through the collection of blood samples and the use of a conventional clinical glucose analyzer. The calibration model can be used subsequently to predict unknown glucose concentrations.
The construction of a successful calibration model requires the extraction of glucose-dependent information from the spectral background produced by the body tissue. Inspections of spectra collected from concentrated glucose solutions reveal significant glucose absorption bands centered at wavelengths of 1.67, 2.13, 2.27, and 2.33 μm. These bands arise from combination and overtone molecular vibrations associated with C—H and O—H bonds of the glucose molecule. The principal near-IR absorbers in tissue are water, proteins, and fat. Each is present in significantly greater quantity than glucose, and the spectral signals arising from each of these species overlap with one or more of the glucose absorption bands. The presence of overlapping spectral signatures dictates that the optical measurement must be made over multiple wavelengths, (Gary Small, Leeos Newsletter, Volume 12, Number 2, April 1998). NIR spectra of aqueous systems show weak, broad and overlapping bands with random baselines. The position and intensity of the signals vary according to the chemical vicinity (hydrogen bonding effects). The influence of dissolved salts and temperature on the NIR spectra of aqueous systems is well known. Since the normal proportion of glucose in blood and tissue is only about 0.1% of the water content the spectral variations due to glucose concentration are extremely small. Due to overlapping interferences and the small size of the glucose signal, the number of wavelengths required for glucose measurement has been said to be at least 12. Thus, the overall signal-to-noise of the raw data comprising a set of light intensity values collected over a series of spectral resolution elements (wavelengths) can affect the ultimate accuracy of the system.
In addition, at wavelengths where the tissue is absorbing strongly, the precision of the optical measurement can be degraded because the amount of light escaping (diffusely reflected) from the tissue does not produce a large signal. Specifically, most of the optical light is absorbed or scattered by the tissue. The simple use of larger more powerful light sources is limited as tissue heating occurs resulting in tissue damage. Although data processing is capable of enhancing the signal-to-noise ratio (SNR) of near infrared spectroscopic data and sophisticated multivariate data processing algorithms (i.e., partial least squares (PLS) regression and/or artificial neural networks (ANN)) are desired to selectively extract the glucose-dependent spectral information, the quality of the raw spectral information drives the ultimate analytical performance and the successful implementation of noninvasive measurements. (Jason J. Burmeister and Mark A. Arnold, “Spectroscopic Considerations for Noninvasive Blood Glucose Measurements with Near Infrared Spectroscopy” Leeos Newsletter, Volume 12, Number 2, April 1998).
Spectroscopic noise introduced by the tissue media is an additional reason for the failure to create a clinically accurate noninvasive system. Tissue noise can include any source of spectroscopic variation that interferes with or hampers accuracy of the analyte measurement. Changes in the optical properties of tissue can contribute to tissue noise. The measurement system itself can also introduce tissue noise, for example changes in the system can make the properties of the tissue appear different. Tissue noise has been well recognized in the published literature, and is variously described as physiological variation, changes in scattering, changes in refractive index, changes in pathlength, changes in water displacement, temperature changes, collagen changes, and changes in the layer nature of tissue. See, e.g., Khalil, Omar: Noninvasive glucose measurement technologies: an update from 1999 to the dawn of the new millennium. Diabetes Technology & Therapeutics, Volume 6, number 5, 2004. Variations in the optical properties of tissue can limit the applicability of conventional spectroscopy to noninvasive measurement. Conventional absorption spectroscopy relies on the Beer-Lambert-Bouger relation between absorption, concentration, pathlength, and molar absorptivity. For the single wavelength, single component case:Iλ=Iλ,o10−ελlc aλ=ελlc Where Iλ,o and Iλ are the incident and excident flux, el is the molar absorptivity, c is the concentration of the species, and l is the pathlength through the medium. al is the absorption at wavelength l (−log10(Iλ/Iλ,o)). These equations assume that photons either pass through the medium with pathlength l, or are absorbed by the molecular occupants.
In tissue, the attenuation of light is described according to light transport theory by the effective attenuation coefficient μeff, i.e.:I=I0e−μeffl Where:μeff=√{square root over (3μa(μa+μ′s))}Light propagation in tissue is governed by a set of spectroscopic properties; the absorption coefficient μa, the scattering coefficient μ5, the refractive index of the cells and the interstitial fluid; and the anisotropy factor g (the average cosine of the angle at which a photon is scattered). Another set of properties are the transport properties, such as the reduced scattering coefficient μ′s, where μ′s=μs[1−g]. The absorption coefficient μa equals the absorbance per unit path length, 2.303 EC cm-1, where E is the molar absorptivity and C is the molar concentration. As one can ascertain from the above equation, changes in tissue scattering and/or tissue absorbance will change the effective path length. As Beer's law assumes a constant pathlength such changes are quite problematic from the perspective of accurate blood glucose measurements.
Unfortunately, optical measurement of tissue does not match the assumptions required by Beer's law. Variations in tissue between individuals, variations in tissue between different locations or different times with the same individual, surface contaminants, interaction of the measurement system with the tissue, and many other real-world effects can prevent accurate optical measurements.
The process of realizing an operational and clinically useful noninvasive glucose monitoring device can require that the system obtains high quality and high signal-to-noise spectra with multiple wavelengths utilizing and optical sampling methodology that effectively samples the tissue without introducing additional variances.